A pioneering work in joint modeling is xu2013convolutional, which performs parameter sharing and captures the relation between SF and IC through Tri-CRF [1]}. The model uses CNN as a shared encoder for both tasks and the produced hidden states are utilized for SF and IC. In addition to features learned from the NN and from the slot label transition, Tri-CRF incorporates an additional factor \(g\)  to learn the correlation between the slot label assigned to each word and the intent assigned to the utterance, which explicitly captures the dependency between the two tasks. A similar approach [2]}, shares the node representation produced by Recursive Neural Network (RecNN) which operates on the syntactic tree of the utterance. The node's representation is shared among SF and IC. Zhang2016AJM use a shared bi-GRU encoder and a joint loss function between SF and IC (Figure REF  Left), in which the loss function has weights associated with each tasks.
